{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "EpistemicUncertainity_using_TFP.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "4G1ty04DZmgD"
      },
      "outputs": [],
      "source": [
        "import tensorflow_probability as tfp\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras import Sequential\n",
        "\n",
        "plt.rcParams.update({'font.size': 22})\n",
        "tfd = tfp.distributions"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def create_dataset(n, x_range):\n",
        "    x_uniform_dist = tfd.Uniform(low=x_range[0], high=x_range[1])\n",
        "    x = x_uniform_dist.sample(n).numpy() [:, np.newaxis] \n",
        "    y_true = 2.7*x+3\n",
        "    eps_uniform_dist = tfd.Normal(loc=0, scale=1)\n",
        "    eps = eps_uniform_dist.sample(n).numpy() [:, np.newaxis] *0.74*x\n",
        "    y = y_true + eps\n",
        "    return x, y, y_true"
      ],
      "metadata": {
        "id": "SWaPaNrLZqQC"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, y_train, y_true = create_dataset(200, [-10, 10])\n",
        "x_val, y_val, _ = create_dataset(50, [-10, 10])"
      ],
      "metadata": {
        "id": "x8_X4l6oZs6p"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def posterior_mean_field(kernel_size, bias_size=0, dtype=None):\n",
        "  n = kernel_size + bias_size\n",
        "  c = np.log(np.expm1(1.))\n",
        "  return Sequential([\n",
        "      tfp.layers.VariableLayer(2 * n, dtype=dtype),\n",
        "      tfp.layers.DistributionLambda(lambda t: tfd.Independent(\n",
        "          tfd.Normal(loc=t[..., :n],\n",
        "                     scale=1e-5 + tf.nn.softplus(c + t[..., n:])),\n",
        "          reinterpreted_batch_ndims=1)),\n",
        "  ])\n",
        "\n"
      ],
      "metadata": {
        "id": "HQfsS9xFaTTw"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def prior_trainable(kernel_size, bias_size=0, dtype=None):\n",
        "  n = kernel_size + bias_size\n",
        "  return Sequential([\n",
        "      tfp.layers.VariableLayer(n, dtype=dtype),\n",
        "      tfp.layers.DistributionLambda(lambda t: tfd.Independent(\n",
        "          tfd.Normal(loc=t, scale=1),\n",
        "          reinterpreted_batch_ndims=1)),\n",
        "  ])"
      ],
      "metadata": {
        "id": "VvRFPE6WaVkS"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "negloglik = lambda y, rv_y: -rv_y.log_prob(y)"
      ],
      "metadata": {
        "id": "HmDVK-Sxa4Pt"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Build model.\n",
        "model = Sequential([\n",
        "  tfp.layers.DenseVariational(1, posterior_mean_field, prior_trainable, kl_weight=1/x_train.shape[0]),\n",
        "  tfp.layers.DistributionLambda(lambda t: tfd.Normal(loc=t, scale=1)),\n",
        "])\n",
        "\n",
        "# Do inference.\n",
        "model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.01), loss=negloglik)\n",
        "model.fit(x_train, y_train, epochs=100, verbose=1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4XV1phBGaX3O",
        "outputId": "7f13b94d-99b5-47b1-8b01-4f96c030d8e6"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "7/7 [==============================] - 2s 5ms/step - loss: 105.9423\n",
            "Epoch 2/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 95.3668\n",
            "Epoch 3/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 165.2451\n",
            "Epoch 4/100\n",
            "7/7 [==============================] - 0s 3ms/step - loss: 84.6878\n",
            "Epoch 5/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 107.3117\n",
            "Epoch 6/100\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 148.8506\n",
            "Epoch 7/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 82.9009\n",
            "Epoch 8/100\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 124.3439\n",
            "Epoch 9/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 86.7174\n",
            "Epoch 10/100\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 114.4306\n",
            "Epoch 11/100\n",
            "7/7 [==============================] - 0s 3ms/step - loss: 86.0139\n",
            "Epoch 12/100\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 101.8409\n",
            "Epoch 13/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 47.5221\n",
            "Epoch 14/100\n",
            "7/7 [==============================] - 0s 5ms/step - loss: 70.0958\n",
            "Epoch 15/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 67.3382\n",
            "Epoch 16/100\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 73.4330\n",
            "Epoch 17/100\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 66.8816\n",
            "Epoch 18/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 122.8234\n",
            "Epoch 19/100\n",
            "7/7 [==============================] - 0s 5ms/step - loss: 48.3485\n",
            "Epoch 20/100\n",
            "7/7 [==============================] - 0s 3ms/step - loss: 50.1044\n",
            "Epoch 21/100\n",
            "7/7 [==============================] - 0s 3ms/step - loss: 44.2613\n",
            "Epoch 22/100\n",
            "7/7 [==============================] - 0s 5ms/step - loss: 45.4326\n",
            "Epoch 23/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 46.7494\n",
            "Epoch 24/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 44.6786\n",
            "Epoch 25/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 42.1986\n",
            "Epoch 26/100\n",
            "7/7 [==============================] - 0s 5ms/step - loss: 75.3863\n",
            "Epoch 27/100\n",
            "7/7 [==============================] - 0s 10ms/step - loss: 31.9284\n",
            "Epoch 28/100\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 52.2608\n",
            "Epoch 29/100\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 22.7110\n",
            "Epoch 30/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 38.8599\n",
            "Epoch 31/100\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 42.4106\n",
            "Epoch 32/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 68.6138\n",
            "Epoch 33/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 34.6534\n",
            "Epoch 34/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 21.2779\n",
            "Epoch 35/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 43.0191\n",
            "Epoch 36/100\n",
            "7/7 [==============================] - 0s 8ms/step - loss: 33.8675\n",
            "Epoch 37/100\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 35.5059\n",
            "Epoch 38/100\n",
            "7/7 [==============================] - 0s 5ms/step - loss: 16.7992\n",
            "Epoch 39/100\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 30.5495\n",
            "Epoch 40/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 28.0394\n",
            "Epoch 41/100\n",
            "7/7 [==============================] - 0s 5ms/step - loss: 23.5324\n",
            "Epoch 42/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 17.3416\n",
            "Epoch 43/100\n",
            "7/7 [==============================] - 0s 3ms/step - loss: 22.4220\n",
            "Epoch 44/100\n",
            "7/7 [==============================] - 0s 3ms/step - loss: 24.1170\n",
            "Epoch 45/100\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 27.6891\n",
            "Epoch 46/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 24.1661\n",
            "Epoch 47/100\n",
            "7/7 [==============================] - 0s 5ms/step - loss: 18.0621\n",
            "Epoch 48/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 25.1593\n",
            "Epoch 49/100\n",
            "7/7 [==============================] - 0s 3ms/step - loss: 18.9544\n",
            "Epoch 50/100\n",
            "7/7 [==============================] - 0s 3ms/step - loss: 18.6385\n",
            "Epoch 51/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 14.7291\n",
            "Epoch 52/100\n",
            "7/7 [==============================] - 0s 11ms/step - loss: 18.5963\n",
            "Epoch 53/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 20.2471\n",
            "Epoch 54/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 15.0606\n",
            "Epoch 55/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 14.8719\n",
            "Epoch 56/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 16.1488\n",
            "Epoch 57/100\n",
            "7/7 [==============================] - 0s 3ms/step - loss: 14.0186\n",
            "Epoch 58/100\n",
            "7/7 [==============================] - 0s 3ms/step - loss: 13.7640\n",
            "Epoch 59/100\n",
            "7/7 [==============================] - 0s 3ms/step - loss: 13.1258\n",
            "Epoch 60/100\n",
            "7/7 [==============================] - 0s 3ms/step - loss: 20.1404\n",
            "Epoch 61/100\n",
            "7/7 [==============================] - 0s 3ms/step - loss: 17.9217\n",
            "Epoch 62/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 13.3381\n",
            "Epoch 63/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 13.8089\n",
            "Epoch 64/100\n",
            "7/7 [==============================] - 0s 3ms/step - loss: 12.7516\n",
            "Epoch 65/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 19.4160\n",
            "Epoch 66/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 15.6475\n",
            "Epoch 67/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 14.3082\n",
            "Epoch 68/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 17.9367\n",
            "Epoch 69/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 16.4367\n",
            "Epoch 70/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 15.6049\n",
            "Epoch 71/100\n",
            "7/7 [==============================] - 0s 3ms/step - loss: 13.5553\n",
            "Epoch 72/100\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 14.2167\n",
            "Epoch 73/100\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 13.7477\n",
            "Epoch 74/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 12.4278\n",
            "Epoch 75/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 10.7623\n",
            "Epoch 76/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 12.2781\n",
            "Epoch 77/100\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 12.8337\n",
            "Epoch 78/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 12.6577\n",
            "Epoch 79/100\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 18.7101\n",
            "Epoch 80/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 14.1303\n",
            "Epoch 81/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 15.9224\n",
            "Epoch 82/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 12.2894\n",
            "Epoch 83/100\n",
            "7/7 [==============================] - 0s 5ms/step - loss: 12.2600\n",
            "Epoch 84/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 14.6504\n",
            "Epoch 85/100\n",
            "7/7 [==============================] - 0s 5ms/step - loss: 12.2607\n",
            "Epoch 86/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 15.1249\n",
            "Epoch 87/100\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 13.8616\n",
            "Epoch 88/100\n",
            "7/7 [==============================] - 0s 3ms/step - loss: 16.4207\n",
            "Epoch 89/100\n",
            "7/7 [==============================] - 0s 7ms/step - loss: 14.5905\n",
            "Epoch 90/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 16.0630\n",
            "Epoch 91/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 16.4947\n",
            "Epoch 92/100\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 11.6828\n",
            "Epoch 93/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 13.2753\n",
            "Epoch 94/100\n",
            "7/7 [==============================] - 0s 3ms/step - loss: 13.1334\n",
            "Epoch 95/100\n",
            "7/7 [==============================] - 0s 5ms/step - loss: 14.7167\n",
            "Epoch 96/100\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 12.6260\n",
            "Epoch 97/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 12.1518\n",
            "Epoch 98/100\n",
            "7/7 [==============================] - 0s 3ms/step - loss: 13.9851\n",
            "Epoch 99/100\n",
            "7/7 [==============================] - 0s 4ms/step - loss: 15.1539\n",
            "Epoch 100/100\n",
            "7/7 [==============================] - 0s 6ms/step - loss: 12.7543\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fb1f01f9050>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_range = [-10, 10]\n",
        "plt.figure(figsize=[10, 10]) \n",
        "plt.clf();\n",
        "plt.plot(x_train, y_train, 'b.', label='observed');\n",
        "\n",
        "y_preds = [model(x_val) for _ in range(100)]\n",
        "avgm = np.zeros_like(x_val[..., 0])\n",
        "for i, yhat in enumerate(y_preds):\n",
        "  m = np.squeeze(yhat.mean())\n",
        "  s = np.squeeze(yhat.stddev())\n",
        "  if i < 15:\n",
        "    plt.plot(x_val, m, 'r', label='ensemble means' if i == 0 else None, linewidth=0.5)\n",
        "  avgm += m\n",
        "plt.plot(x_val, avgm/len(y_preds), 'g', label='overall mean', linewidth=4)\n",
        "plt.xticks(fontsize=20)\n",
        "plt.yticks(fontsize=20)\n",
        "plt.legend(loc='upper left', prop={'size': 20})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "c2WUu1DgabWF",
        "outputId": "a0309972-4631-4424-dcec-dd3deb255150"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fb1820da050>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "O50wwp3Iag9D"
      },
      "execution_count": 8,
      "outputs": []
    }
  ]
}